// Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Gemini CLI's capabilities with specialized knowledge, workflows, or tool integrations.
| name | skill-creator |
| description | Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Gemini CLI's capabilities with specialized knowledge, workflows, or tool integrations. |
This skill provides guidance for creating effective skills.
Skills are modular, self-contained packages that extend Gemini CLI's capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform Gemini CLI from a general-purpose agent into a specialized agent equipped with procedural knowledge that no model can fully possess.
The context window is a public good. Skills share the context window with everything else Gemini CLI needs: system prompt, conversation history, other Skills' metadata, and the actual user request.
Default assumption: Gemini CLI is already very smart. Only add context Gemini CLI doesn't already have. Challenge each piece of information: "Does Gemini CLI really need this explanation?" and "Does this paragraph justify its token cost?"
Prefer concise examples over verbose explanations.
Match the level of specificity to the task's fragility and variability:
High freedom (text-based instructions): Use when multiple approaches are valid, decisions depend on context, or heuristics guide the approach.
Medium freedom (pseudocode or scripts with parameters): Use when a preferred pattern exists, some variation is acceptable, or configuration affects behavior.
Low freedom (specific scripts, few parameters): Use when operations are fragile and error-prone, consistency is critical, or a specific sequence must be followed.
Think of Gemini CLI as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).
Every skill consists of a required SKILL.md file and optional bundled resources:
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter metadata (required)
│ │ ├── name: (required)
│ │ └── description: (required)
│ └── Markdown instructions (required)
└── Bundled Resources (optional)
├── scripts/ - Executable code (Node.js/Python/Bash/etc.)
├── references/ - Documentation intended to be loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts, etc.)
Every SKILL.md consists of:
name and description fields. These are the only fields that Gemini CLI reads to determine when the skill gets used, thus it is very important to be clear and comprehensive in describing what the skill is, and when it should be used.scripts/)Executable code (Node.js/Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
scripts/rotate_pdf.py for PDF rotation tasksreferences/)Documentation and reference material intended to be loaded as needed into context to inform Gemini CLI's process and thinking.
references/finance.md for financial schemas, references/mnda.md for company NDA template, references/policies.md for company policies, references/api_docs.md for API specificationsassets/)Files not intended to be loaded into context, but rather used within the output Gemini CLI produces.
assets/logo.png for brand assets, assets/slides.pptx for PowerPoint templates, assets/frontend-template/ for HTML/React boilerplate, assets/font.ttf for typographyA skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:
The skill should only contain the information needed for an AI agent to do the job at hand. It should not contain auxiliary context about the process that went into creating it, setup and testing procedures, user-facing documentation, etc. Creating additional documentation files just adds clutter and confusion.
Skills use a three-level loading system to manage context efficiently:
Keep SKILL.md body to the essentials and under 500 lines to minimize context bloat. Split content into separate files when approaching this limit. When splitting out content into other files, it is very important to reference them from SKILL.md and describe clearly when to read them, to ensure the reader of the skill knows they exist and when to use them.
Key principle: When a skill supports multiple variations, frameworks, or options, keep only the core workflow and selection guidance in SKILL.md. Move variant-specific details (patterns, examples, configuration) into separate reference files.
Pattern 1: High-level guide with references
# PDF Processing
## Quick start
Extract text with pdfplumber: [code example]
## Advanced features
- **Form filling**: See [FORMS.md](FORMS.md) for complete guide
- **API reference**: See [REFERENCE.md](REFERENCE.md) for all methods
- **Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns
Gemini CLI loads FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed.
Pattern 2: Domain-specific organization
For Skills with multiple domains, organize content by domain to avoid loading irrelevant context:
bigquery-skill/
├── SKILL.md (overview and navigation)
└── reference/
├── finance.md (revenue, billing metrics)
├── sales.md (opportunities, pipeline)
├── product.md (API usage, features)
└── marketing.md (campaigns, attribution)
When a user asks about sales metrics, Gemini CLI only reads sales.md.
Similarly, for skills supporting multiple frameworks or variants, organize by variant:
cloud-deploy/
├── SKILL.md (workflow + provider selection)
└── references/
├── aws.md (AWS deployment patterns)
├── gcp.md (GCP deployment patterns)
└── azure.md (Azure deployment patterns)
When the user chooses AWS, Gemini CLI only reads aws.md.
Pattern 3: Conditional details
Show basic content, link to advanced content:
# CSV Processing
## Basic Analysis
Use pandas for loading and basic queries. See [PANDAS.md](PANDAS.md).
## Advanced Operations
For massive files that exceed memory, see [STREAMING.md](STREAMING.md). For timestamp normalization, see [TIMESTAMPS.md](TIMESTAMPS.md).
Gemini CLI reads REDLINING.md or OOXML.md only when the user needs those features.
Important guidelines:
Skill creation involves these steps:
Follow these steps in order, skipping only if there is a clear reason why they are not applicable.
plan-mode).gh-address-comments, linear-address-issue).Skip this step only when the skill's usage patterns are already clearly understood. It remains valuable even when working with an existing skill.
To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.
For example, when building an image-editor skill, relevant questions include:
Avoid interrogation loops: Do not ask more than one or two clarifying questions at a time. Bias toward action: propose a concrete list of features or examples based on your initial understanding, and ask the user to refine them.
Conclude this step when there is a clear sense of the functionality the skill should support.
To turn concrete examples into an effective skill, analyze each example by:
Example: When building a pdf-editor skill to handle queries like "Help me rotate this PDF," the analysis shows:
scripts/rotate_pdf.py script would be helpful to store in the skillExample: When designing a frontend-webapp-builder skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:
assets/hello-world/ template containing the boilerplate HTML/React project files would be helpful to store in the skillExample: When building a big-query skill to handle queries like "How many users have logged in today?" the analysis shows:
references/schema.md file documenting the table schemas would be helpful to store in the skillTo establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
At this point, it is time to actually create the skill.
Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
When creating a new skill from scratch, always run the init_skill.py script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
Note: Use the absolute path to the script as provided in the available_resources section.
Usage:
python3 <path-to-skill-creator>/scripts/init_skill.py <skill-name> --path <output-directory>
The script:
scripts/, references/, and assets/scripts/example_script.py, references/example_reference.md, assets/example_asset.txt) that can be customized or deletedAfter initialization, customize or remove the generated SKILL.md and example files as needed.
When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of Gemini CLI to use. Include information that would be beneficial and non-obvious to Gemini CLI. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Gemini CLI instance execute these tasks more effectively.
Consult these helpful guides based on your skill's needs:
These files contain established best practices for effective skill design.
To begin implementation, start with the reusable resources identified above: scripts/, references/, and assets/ files. Note that this step may require user input. For example, when implementing a brand-guidelines skill, the user may need to provide brand assets or templates to store in assets/, or documentation to store in references/.
Added scripts must be tested by actually running them to ensure there are no bugs and that the output matches what is expected. If there are many similar scripts, only a representative sample needs to be tested to ensure confidence that they all work while balancing time to completion.
Any example files and directories not needed for the skill should be deleted. The initialization script creates example files in scripts/, references/, and assets/ to demonstrate structure, but most skills won't need all of them.
Writing Guidelines: Always use imperative/infinitive form.
Write the YAML frontmatter with name and description:
name: The skill namedescription: This is the primary triggering mechanism for your skill, and helps Gemini CLI understand when to use the skill.
description: Data ingestion...). Quotes are optional.description: Data ingestion, cleaning, and transformation for tabular data. Use when Gemini CLI needs to work with CSV/TSV files to analyze large datasets, normalize schemas, or merge sources.Do not include any other fields in YAML frontmatter.
Write instructions for using the skill and its bundled resources.
Once development of the skill is complete, it must be packaged into a distributable .skill file that gets shared with the user. The packaging process automatically validates the skill first (checking YAML and ensuring no TODOs remain) to ensure it meets all requirements:
Note: Use the absolute path to the script as provided in the available_resources section.
python3 <path-to-skill-creator>/scripts/package_skill.py <path/to/skill-folder>
Optional output directory specification:
python3 <path-to-skill-creator>/scripts/package_skill.py <path/to/skill-folder> ./dist
The packaging script will:
Validate the skill automatically, checking:
Package the skill if validation passes, creating a .skill file named after the skill (e.g., my-skill.skill) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.
If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
Once the skill is packaged into a .skill file, offer to install it for the user. Ask whether they would like to install it locally in the current folder (workspace scope) or at the user level (user scope).
If the user agrees to an installation, perform it immediately using the run_shell_command tool:
gemini skills install <path/to/skill-name.skill> --scope workspace
gemini skills install <path/to/skill-name.skill> --scope user
Important: After the installation is complete, notify the user that they MUST manually execute the /skills reload command in their interactive Gemini CLI session to enable the new skill. They can then verify the installation by running /skills list.
Note: You (the agent) cannot execute the /skills reload command yourself; it must be done by the user in an interactive instance of Gemini CLI. Do not attempt to run it on their behalf.
After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.
Iteration workflow:
Automates version bumping in pyproject.toml and creating GitHub releases. Use when releasing a new version of the project.
Guided feature development with codebase understanding and architecture focus
Use standardized scripts to manage the project environment, testing, and linting.